In [1]:
# Computations
import numpy as np
import pandas as pd

# scipy
import scipy.stats as stats

# sklearn
from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, cross_val_score

# keras
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.utils.vis_utils import plot_model
import keras.backend as K

# Visualisation libraries
import seaborn as sns

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec

import missingno as msno

import plotly.offline as py
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot 
from plotly.subplots import make_subplots
from wordcloud import WordCloud
import re
# Graphics in retina format 
%config InlineBackend.figure_format = 'retina' 

# sns setting
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})
sns.set_style("whitegrid")

# plt setting
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
Using TensorFlow backend.

Pima Indians Diabetes Data Classification using Feature Importance

In this article, we use Kaggle'sPima Indians Diabetes. The Pima indians are a group of Native Americans living in an area consisting of what is now central and southern Arizona. A variety of statistical methods are used here for predictions.

Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Content

The datasets consist of several medical predictor variables and one target variable, Outcome. Predictor variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Citations

Table of contents

Dataset Analysis

In [2]:
Data = pd.read_csv('pima-indians-diabetes-database/diabetes.csv')
display(Data.head())

print('The Dataset Shape: %i rows and %i columns' % Data.shape)
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
The Dataset Shape: 768 rows and 9 columns
Feature Explanations
Pregnancies Number of times pregnant
Glucose Plasma glucose concentration a 2 hours in an oral glucose tolerance test
BloodPressure Diastolic blood pressure (mm Hg)
SkinThickness Triceps skinfold thickness (mm)
Insulin 2-Hour serum insulin (mu U/ml)
BMI Body mass index (weight in kg/(height in m)^2)
DiabetesPedigreeFunction Diabetes pedigree function
Age Age (years)
Outcome Whether or not a patient has diabetes
In [3]:
def Data_info(Inp, Only_NaN = False):
    Out = pd.DataFrame(Inp.dtypes,columns=['Data Type']).sort_values(by=['Data Type'])
    Out = Out.join(pd.DataFrame(Inp.isnull().sum(), columns=['Number of NaN Values']), how='outer')
    Out['Percentage'] = np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out
display(Data_info(Data).T[:2])
_ = msno.bar(Data, figsize=(12,3), fontsize=14, log=False, color="#34495e")
display(Data.describe())
Age BMI BloodPressure DiabetesPedigreeFunction Glucose Insulin Outcome Pregnancies SkinThickness
Data Type int64 float64 int64 float64 int64 int64 int64 int64 int64
Number of NaN Values 0 0 0 0 0 0 0 0 0
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000

Let's take a close look at our data.

In [4]:
fig, ax = plt.subplots(nrows=4, ncols=2, figsize = (16, 20))
for i in range(len(Data.columns[:-1])):
    sns.distplot(Data.iloc[:,i], rug=True, rug_kws={"color": "red"},
                 kde_kws={"color": "k", "lw": 2, "label": "KDE"},
                 hist_kws={"histtype": "step", "linewidth": 2,
                           "alpha": 1, "color": "Navy"}, ax= ax[int(i/2),i%2])
    if Data.iloc[:,i].name != 'BMI':
        ax[int(i/2),i%2].set_xlabel(re.sub(r"(\w)([A-Z])", r"\1 \2", Data.iloc[:,i].name))
In [5]:
Temp = ['Non-Diabetic' if x==0 else 'Diabetic' for x in Data['Outcome']]

fig = go.Figure(data=go.Splom(dimensions=[dict(label='Pregnancies', values=Data['Pregnancies']),
                              dict(label='Glucose', values=Data['Glucose']),
                              dict(label='Blood<br>Pressure', values=Data['BloodPressure']),
                              dict(label='Skin<br>Thickness', values=Data['SkinThickness']),
                              dict(label='Insulin', values=Data['Insulin']),
                              dict(label='BMI', values=Data['BMI']),
                              dict(label='Diabetes<br>Pedigree<br>Fun', values=Data['DiabetesPedigreeFunction']),
                              dict(label='Age', values=Data['Age'])],
                              showupperhalf=False,
                              marker=dict(color=Data['Outcome'], size=4, colorscale='Bluered',
                              line=dict(width=0.4, color='black')),
                              text=Temp, diagonal=dict(visible=False)))
del Temp
fig.update_layout(title='Scatterplot Matrix', dragmode='select',
                  width=900, height=900, hovermode='closest')
fig.show()

As can be seen, the Data has a normal distribution, and some entries need to be adjusted. In doing so, we defined a normalizer as follows, for a given vector $x$,

\begin{align*} \text{Normalizer}(x, cut) = \begin{cases} x_i &\mbox{if } |x_i- \mu|<\sigma\times cut \\ mode(x) & \mbox{else} \end{cases}. \end{align*}
In [6]:
def Normalizer(Col, cut = 3):
    return Col[(Col > (Col.mean() - Col.std() * cut)) &
               (Col < (Col.mean() + Col.std() * cut))]

fig, ax = plt.subplots(nrows=4, ncols=2, figsize = (16, 20))
Temp = Data.copy()
for i in range(len(Data.columns[:-1])):
    Data[Data.columns[i]] = Normalizer(Data[Data.columns[i]])
    Data[Data.columns[i]] = Data[Data.columns[i]].fillna(Data[Data.columns[i]].dropna().mode()[0])
    # Sub-Plots
    sns.distplot(Data.iloc[:,i], rug=True, rug_kws={"color": "red"},
                 kde_kws={"color": "k", "lw": 2, "label": "KDE"},
                 hist_kws={"histtype": "step", "linewidth": 2,
                           "alpha": 1, "color": "Navy"}, ax= ax[int(i/2),i%2])
    if Data.iloc[:,i].name != 'BMI':
        ax[int(i/2),i%2].set_xlabel(re.sub(r"(\w)([A-Z])", r"\1 \2", Data.iloc[:,i].name))

Basically, we diminished the influence of certain data points (see the following figure).

In [7]:
Temp0 = Temp.copy()
Temp0.iloc[:,:-1] = abs(Data.iloc[:,:-1] - Temp.iloc[:,:-1])

Temp = ['Non-Diabetic' if x==0 else 'Diabetic' for x in Temp0['Outcome']]

fig = go.Figure(data=go.Splom(dimensions=[dict(label='Pregnancies', values=Temp0['Pregnancies']),
                              dict(label='Glucose', values=Temp0['Glucose']),
                              dict(label='Blood<br>Pressure', values=Temp0['BloodPressure']),
                              dict(label='Skin<br>Thickness', values=Temp0['SkinThickness']),
                              dict(label='Insulin', values=Temp0['Insulin']),
                              dict(label='BMI', values=Temp0['BMI']),
                              dict(label='Diabetes<br>Pedigree<br>Fun', values=Temp0['DiabetesPedigreeFunction']),
                              dict(label='Age', values=Temp0['Age'])],
                              showupperhalf=False,
                              marker=dict(color=Temp0['Outcome'], size=4, colorscale='Bluered',
                              line=dict(width=0.4, color='black')),
                              text=Temp, diagonal=dict(visible=False)))
del Temp, Temp0
fig.update_layout(title='Scatterplot Matrix', dragmode='select',
                  width=900, height=900, hovermode='closest')
fig.show()

Data Correlation

In [8]:
def Correlation_Plot (Df,Fig_Size):
    Correlation_Matrix = Df.corr()
    mask = np.zeros_like(Correlation_Matrix)
    mask[np.triu_indices_from(mask)] = True
    for i in range(len(mask)):
        mask[i,i]=0
    Fig, ax = plt.subplots(figsize=(Fig_Size,Fig_Size))
    sns.heatmap(Correlation_Matrix, ax=ax, mask=mask, annot=True, square=True, 
                cmap =sns.color_palette("RdYlGn", n_colors=10), linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": .7})
    bottom, top = ax.get_ylim()

Correlation_Plot (Data, 9)
In [9]:
Temp = Data.iloc[:,:-1].var().sort_values(ascending = False).to_frame(name= 'Variance')
display(Temp)
Temp0 = Data.corr()
Temp0.loc[Temp.index[-1]].sort_values().to_frame(name= 'Correlation')[:-1].T
Variance
Insulin 7844.510917
Glucose 929.680350
SkinThickness 246.979708
BloodPressure 146.573540
Age 128.991301
BMI 43.941176
Pregnancies 10.734190
DiabetesPedigreeFunction 0.078702
Out[9]:
Pregnancies BloodPressure Age Glucose BMI SkinThickness Insulin Outcome
Correlation 0.015703 0.034428 0.066525 0.095686 0.122868 0.15229 0.184028 0.192156

Even though the variance of Diabetes Pedigree Function is low, this might not improve the performance of the model, the correlation of this feature with the reset of features, especially with the Outcome, is noticeable.

Modeling and Classification

In [10]:
Target = 'Outcome'

X = Data.drop(columns = [Target])
y = Data[Target]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

pd.DataFrame(data={'Set':['X_train','X_test','y_train','y_test'],
               'Shape':[X_train.shape, X_test.shape, y_train.shape, y_test.shape]}).set_index('Set').T
Out[10]:
Set X_train X_test y_train y_test
Shape (537, 8) (231, 8) (537,) (231,)

Furthermore, we would like to standardize features by removing the mean and scaling to unit variance.

In [11]:
scaler = StandardScaler()

X_train_STD = scaler.fit_transform(X_train)
X_test_STD = scaler.transform(X_test)

X_train_STD = pd.DataFrame(data = X_train_STD, columns = X_train.columns)
X_test_STD = pd.DataFrame(data = X_test_STD, columns = X_test.columns)

Here, we implement an artificial neural network (ANN) using Keras.

In [12]:
model = Sequential()
model.add(Dense(12, input_dim= X_train_STD.shape[1], init='uniform', activation='relu'))
model.add(Dense(10, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='relu'))
# Number of iterations
N = int(1e3)
def mean_pred(y_true, y_pred):
    return K.mean(y_pred)

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy', mean_pred])

# Train model
history = model.fit(X_train_STD, y_train, nb_epoch= N, batch_size=50,  verbose=1)
# Predications and Score
y_pred = model.predict(X_test_STD)
score = model.evaluate(X_test_STD, y_test) 
Epoch 1/1000
537/537 [==============================] - 0s 164us/step - loss: 1.7979 - accuracy: 0.6499 - mean_pred: 0.0065
Epoch 2/1000
537/537 [==============================] - 0s 19us/step - loss: 1.4491 - accuracy: 0.6499 - mean_pred: 0.0141
Epoch 3/1000
537/537 [==============================] - 0s 19us/step - loss: 1.2474 - accuracy: 0.6499 - mean_pred: 0.0247
Epoch 4/1000
537/537 [==============================] - 0s 17us/step - loss: 1.0799 - accuracy: 0.6499 - mean_pred: 0.0393
Epoch 5/1000
537/537 [==============================] - 0s 17us/step - loss: 0.9383 - accuracy: 0.6499 - mean_pred: 0.0601
Epoch 6/1000
537/537 [==============================] - 0s 17us/step - loss: 0.8212 - accuracy: 0.6499 - mean_pred: 0.0869
Epoch 7/1000
537/537 [==============================] - 0s 15us/step - loss: 0.7214 - accuracy: 0.6499 - mean_pred: 0.1187
Epoch 8/1000
537/537 [==============================] - 0s 19us/step - loss: 0.6362 - accuracy: 0.6555 - mean_pred: 0.1584
Epoch 9/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5691 - accuracy: 0.6946 - mean_pred: 0.2060
Epoch 10/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5249 - accuracy: 0.7207 - mean_pred: 0.2544
Epoch 11/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5445 - accuracy: 0.7467 - mean_pred: 0.3015
Epoch 12/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5287 - accuracy: 0.7635 - mean_pred: 0.3381
Epoch 13/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5251 - accuracy: 0.7561 - mean_pred: 0.3371
Epoch 14/1000
537/537 [==============================] - 0s 15us/step - loss: 0.5177 - accuracy: 0.7561 - mean_pred: 0.3347
Epoch 15/1000
537/537 [==============================] - 0s 15us/step - loss: 0.5151 - accuracy: 0.7598 - mean_pred: 0.3442
Epoch 16/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5334 - accuracy: 0.7635 - mean_pred: 0.3593
Epoch 17/1000
537/537 [==============================] - 0s 19us/step - loss: 0.5301 - accuracy: 0.7635 - mean_pred: 0.3497
Epoch 18/1000
537/537 [==============================] - 0s 15us/step - loss: 0.5256 - accuracy: 0.7672 - mean_pred: 0.3496
Epoch 19/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5225 - accuracy: 0.7784 - mean_pred: 0.3518
Epoch 20/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5205 - accuracy: 0.7821 - mean_pred: 0.3464
Epoch 21/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5187 - accuracy: 0.7784 - mean_pred: 0.3414
Epoch 22/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5154 - accuracy: 0.7765 - mean_pred: 0.3416
Epoch 23/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5181 - accuracy: 0.7765 - mean_pred: 0.3395
Epoch 24/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4991 - accuracy: 0.7803 - mean_pred: 0.3240
Epoch 25/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4947 - accuracy: 0.7709 - mean_pred: 0.3022
Epoch 26/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4925 - accuracy: 0.7747 - mean_pred: 0.3104
Epoch 27/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4904 - accuracy: 0.7784 - mean_pred: 0.3194
Epoch 28/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4884 - accuracy: 0.7803 - mean_pred: 0.3218
Epoch 29/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4874 - accuracy: 0.7877 - mean_pred: 0.3442
Epoch 30/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4893 - accuracy: 0.7765 - mean_pred: 0.3267
Epoch 31/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4920 - accuracy: 0.7803 - mean_pred: 0.3303
Epoch 32/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4864 - accuracy: 0.7821 - mean_pred: 0.3235
Epoch 33/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4858 - accuracy: 0.7840 - mean_pred: 0.3336
Epoch 34/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4866 - accuracy: 0.7821 - mean_pred: 0.3265
Epoch 35/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4865 - accuracy: 0.7877 - mean_pred: 0.3279
Epoch 36/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4854 - accuracy: 0.7821 - mean_pred: 0.3302
Epoch 37/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4834 - accuracy: 0.7858 - mean_pred: 0.3375
Epoch 38/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4816 - accuracy: 0.7821 - mean_pred: 0.3313
Epoch 39/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5027 - accuracy: 0.7858 - mean_pred: 0.3419
Epoch 40/1000
537/537 [==============================] - 0s 17us/step - loss: 0.5034 - accuracy: 0.7858 - mean_pred: 0.3464
Epoch 41/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4996 - accuracy: 0.7858 - mean_pred: 0.3456
Epoch 42/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4998 - accuracy: 0.7858 - mean_pred: 0.3367
Epoch 43/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4990 - accuracy: 0.7896 - mean_pred: 0.3452
Epoch 44/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4974 - accuracy: 0.7877 - mean_pred: 0.3495
Epoch 45/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4985 - accuracy: 0.7858 - mean_pred: 0.3474
Epoch 46/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4973 - accuracy: 0.7840 - mean_pred: 0.3393
Epoch 47/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4976 - accuracy: 0.7803 - mean_pred: 0.3560
Epoch 48/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4956 - accuracy: 0.7821 - mean_pred: 0.3395
Epoch 49/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4953 - accuracy: 0.7784 - mean_pred: 0.3393
Epoch 50/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4960 - accuracy: 0.7803 - mean_pred: 0.3498
Epoch 51/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4943 - accuracy: 0.7821 - mean_pred: 0.3570
Epoch 52/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4948 - accuracy: 0.7784 - mean_pred: 0.3376
Epoch 53/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4945 - accuracy: 0.7821 - mean_pred: 0.3501
Epoch 54/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4951 - accuracy: 0.7784 - mean_pred: 0.3491
Epoch 55/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4942 - accuracy: 0.7821 - mean_pred: 0.3323
Epoch 56/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4937 - accuracy: 0.7784 - mean_pred: 0.3519
Epoch 57/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4938 - accuracy: 0.7803 - mean_pred: 0.3470
Epoch 58/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4949 - accuracy: 0.7803 - mean_pred: 0.3480
Epoch 59/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4930 - accuracy: 0.7821 - mean_pred: 0.3506
Epoch 60/1000
537/537 [==============================] - 0s 13us/step - loss: 0.4931 - accuracy: 0.7765 - mean_pred: 0.3467
Epoch 61/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4935 - accuracy: 0.7803 - mean_pred: 0.3483
Epoch 62/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4924 - accuracy: 0.7821 - mean_pred: 0.3469
Epoch 63/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4933 - accuracy: 0.7784 - mean_pred: 0.3385
Epoch 64/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4926 - accuracy: 0.7784 - mean_pred: 0.3466
Epoch 65/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4919 - accuracy: 0.7821 - mean_pred: 0.3497
Epoch 66/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4922 - accuracy: 0.7821 - mean_pred: 0.3446
Epoch 67/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4917 - accuracy: 0.7858 - mean_pred: 0.3550
Epoch 68/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4926 - accuracy: 0.7747 - mean_pred: 0.3462
Epoch 69/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4915 - accuracy: 0.7784 - mean_pred: 0.3449
Epoch 70/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4911 - accuracy: 0.7896 - mean_pred: 0.3551
Epoch 71/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4914 - accuracy: 0.7765 - mean_pred: 0.3450
Epoch 72/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4912 - accuracy: 0.7765 - mean_pred: 0.3351
Epoch 73/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4900 - accuracy: 0.7858 - mean_pred: 0.3654
Epoch 74/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4903 - accuracy: 0.7765 - mean_pred: 0.3331
Epoch 75/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4902 - accuracy: 0.7840 - mean_pred: 0.3532
Epoch 76/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4900 - accuracy: 0.7821 - mean_pred: 0.3427
Epoch 77/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4902 - accuracy: 0.7784 - mean_pred: 0.3483
Epoch 78/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4893 - accuracy: 0.7821 - mean_pred: 0.3469
Epoch 79/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4900 - accuracy: 0.7784 - mean_pred: 0.3484
Epoch 80/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4898 - accuracy: 0.7840 - mean_pred: 0.3469
Epoch 81/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4898 - accuracy: 0.7877 - mean_pred: 0.3589
Epoch 82/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4912 - accuracy: 0.7784 - mean_pred: 0.3495
Epoch 83/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4902 - accuracy: 0.7821 - mean_pred: 0.3414
Epoch 84/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4889 - accuracy: 0.7803 - mean_pred: 0.3474
Epoch 85/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4879 - accuracy: 0.7840 - mean_pred: 0.3502
Epoch 86/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4881 - accuracy: 0.7765 - mean_pred: 0.3446
Epoch 87/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4891 - accuracy: 0.7765 - mean_pred: 0.3469
Epoch 88/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4887 - accuracy: 0.7784 - mean_pred: 0.3490
Epoch 89/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4887 - accuracy: 0.7821 - mean_pred: 0.3548
Epoch 90/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4902 - accuracy: 0.7784 - mean_pred: 0.3521
Epoch 91/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4877 - accuracy: 0.7821 - mean_pred: 0.3535
Epoch 92/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4878 - accuracy: 0.7803 - mean_pred: 0.3477
Epoch 93/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4882 - accuracy: 0.7784 - mean_pred: 0.3484
Epoch 94/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4892 - accuracy: 0.7747 - mean_pred: 0.3592
Epoch 95/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4868 - accuracy: 0.7803 - mean_pred: 0.3417
Epoch 96/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4874 - accuracy: 0.7821 - mean_pred: 0.3535
Epoch 97/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4881 - accuracy: 0.7803 - mean_pred: 0.3525
Epoch 98/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4874 - accuracy: 0.7803 - mean_pred: 0.3515
Epoch 99/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4870 - accuracy: 0.7821 - mean_pred: 0.3353
Epoch 100/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4877 - accuracy: 0.7821 - mean_pred: 0.3504
Epoch 101/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4867 - accuracy: 0.7803 - mean_pred: 0.3474
Epoch 102/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4864 - accuracy: 0.7803 - mean_pred: 0.3484
Epoch 103/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4866 - accuracy: 0.7821 - mean_pred: 0.3569
Epoch 104/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4856 - accuracy: 0.7858 - mean_pred: 0.3429
Epoch 105/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4879 - accuracy: 0.7784 - mean_pred: 0.3510
Epoch 106/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4860 - accuracy: 0.7765 - mean_pred: 0.3453
Epoch 107/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4853 - accuracy: 0.7803 - mean_pred: 0.3464
Epoch 108/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4862 - accuracy: 0.7784 - mean_pred: 0.3462
Epoch 109/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4857 - accuracy: 0.7784 - mean_pred: 0.3552
Epoch 110/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4850 - accuracy: 0.7784 - mean_pred: 0.3444
Epoch 111/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4851 - accuracy: 0.7840 - mean_pred: 0.3566
Epoch 112/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4843 - accuracy: 0.7821 - mean_pred: 0.3496
Epoch 113/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4853 - accuracy: 0.7765 - mean_pred: 0.3526
Epoch 114/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4850 - accuracy: 0.7840 - mean_pred: 0.3553
Epoch 115/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4848 - accuracy: 0.7821 - mean_pred: 0.3593
Epoch 116/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4836 - accuracy: 0.7803 - mean_pred: 0.3484
Epoch 117/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4854 - accuracy: 0.7821 - mean_pred: 0.3580
Epoch 118/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4846 - accuracy: 0.7784 - mean_pred: 0.3456
Epoch 119/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4835 - accuracy: 0.7803 - mean_pred: 0.3438
Epoch 120/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4843 - accuracy: 0.7858 - mean_pred: 0.3615
Epoch 121/1000
537/537 [==============================] - 0s 13us/step - loss: 0.4838 - accuracy: 0.7803 - mean_pred: 0.3477
Epoch 122/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4837 - accuracy: 0.7803 - mean_pred: 0.3509
Epoch 123/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4827 - accuracy: 0.7821 - mean_pred: 0.3454
Epoch 124/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4837 - accuracy: 0.7858 - mean_pred: 0.3619
Epoch 125/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4844 - accuracy: 0.7821 - mean_pred: 0.3505
Epoch 126/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4842 - accuracy: 0.7803 - mean_pred: 0.3529
Epoch 127/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4816 - accuracy: 0.7840 - mean_pred: 0.3460
Epoch 128/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4832 - accuracy: 0.7840 - mean_pred: 0.3585
Epoch 129/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4829 - accuracy: 0.7821 - mean_pred: 0.3520
Epoch 130/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4821 - accuracy: 0.7803 - mean_pred: 0.3473
Epoch 131/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4818 - accuracy: 0.7896 - mean_pred: 0.3486
Epoch 132/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4831 - accuracy: 0.7821 - mean_pred: 0.3510
Epoch 133/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4833 - accuracy: 0.7784 - mean_pred: 0.3559
Epoch 134/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4809 - accuracy: 0.7821 - mean_pred: 0.3495
Epoch 135/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4814 - accuracy: 0.7784 - mean_pred: 0.3505
Epoch 136/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4825 - accuracy: 0.7858 - mean_pred: 0.3543
Epoch 137/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4819 - accuracy: 0.7858 - mean_pred: 0.3525
Epoch 138/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4806 - accuracy: 0.7803 - mean_pred: 0.3434
Epoch 139/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4816 - accuracy: 0.7858 - mean_pred: 0.3571
Epoch 140/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4798 - accuracy: 0.7877 - mean_pred: 0.3429
Epoch 141/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4812 - accuracy: 0.7821 - mean_pred: 0.3534
Epoch 142/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4804 - accuracy: 0.7858 - mean_pred: 0.3602
Epoch 143/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4803 - accuracy: 0.7877 - mean_pred: 0.3554
Epoch 144/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4793 - accuracy: 0.7877 - mean_pred: 0.3451
Epoch 145/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4805 - accuracy: 0.7803 - mean_pred: 0.3588
Epoch 146/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4806 - accuracy: 0.7858 - mean_pred: 0.3498
Epoch 147/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4799 - accuracy: 0.7858 - mean_pred: 0.3540
Epoch 148/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4795 - accuracy: 0.7858 - mean_pred: 0.3597
Epoch 149/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4807 - accuracy: 0.7803 - mean_pred: 0.3418
Epoch 150/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4790 - accuracy: 0.7877 - mean_pred: 0.3557
Epoch 151/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4786 - accuracy: 0.7840 - mean_pred: 0.3544
Epoch 152/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4805 - accuracy: 0.7821 - mean_pred: 0.3494
Epoch 153/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4801 - accuracy: 0.7858 - mean_pred: 0.3494
Epoch 154/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4791 - accuracy: 0.7858 - mean_pred: 0.3569
Epoch 155/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4786 - accuracy: 0.7765 - mean_pred: 0.3477
Epoch 156/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4796 - accuracy: 0.7877 - mean_pred: 0.3579
Epoch 157/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4794 - accuracy: 0.7840 - mean_pred: 0.3494
Epoch 158/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4785 - accuracy: 0.7840 - mean_pred: 0.3509
Epoch 159/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4799 - accuracy: 0.7877 - mean_pred: 0.3546
Epoch 160/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4779 - accuracy: 0.7896 - mean_pred: 0.3576
Epoch 161/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4789 - accuracy: 0.7914 - mean_pred: 0.3551
Epoch 162/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4778 - accuracy: 0.7858 - mean_pred: 0.3562
Epoch 163/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4773 - accuracy: 0.7840 - mean_pred: 0.3580
Epoch 164/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4802 - accuracy: 0.7877 - mean_pred: 0.3613
Epoch 165/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4775 - accuracy: 0.7858 - mean_pred: 0.3533
Epoch 166/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4771 - accuracy: 0.7840 - mean_pred: 0.3524
Epoch 167/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4775 - accuracy: 0.7877 - mean_pred: 0.3502
Epoch 168/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4771 - accuracy: 0.7896 - mean_pred: 0.3541
Epoch 169/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4759 - accuracy: 0.7896 - mean_pred: 0.3644
Epoch 170/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4768 - accuracy: 0.7858 - mean_pred: 0.3473
Epoch 171/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4772 - accuracy: 0.7821 - mean_pred: 0.3446
Epoch 172/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4760 - accuracy: 0.7896 - mean_pred: 0.3601
Epoch 173/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4755 - accuracy: 0.7858 - mean_pred: 0.3539
Epoch 174/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4762 - accuracy: 0.7914 - mean_pred: 0.3600
Epoch 175/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4747 - accuracy: 0.7914 - mean_pred: 0.3620
Epoch 176/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4750 - accuracy: 0.7858 - mean_pred: 0.3418
Epoch 177/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4759 - accuracy: 0.7877 - mean_pred: 0.3557
Epoch 178/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4748 - accuracy: 0.7914 - mean_pred: 0.3548
Epoch 179/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4761 - accuracy: 0.7914 - mean_pred: 0.3548
Epoch 180/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4741 - accuracy: 0.7933 - mean_pred: 0.3540
Epoch 181/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4768 - accuracy: 0.7877 - mean_pred: 0.3568
Epoch 182/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4738 - accuracy: 0.7896 - mean_pred: 0.3555
Epoch 183/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4739 - accuracy: 0.7896 - mean_pred: 0.3510
Epoch 184/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4745 - accuracy: 0.7858 - mean_pred: 0.3589
Epoch 185/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4740 - accuracy: 0.7877 - mean_pred: 0.3530
Epoch 186/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4737 - accuracy: 0.7970 - mean_pred: 0.3545
Epoch 187/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4739 - accuracy: 0.7914 - mean_pred: 0.3593
Epoch 188/1000
537/537 [==============================] - 0s 13us/step - loss: 0.4734 - accuracy: 0.7914 - mean_pred: 0.3573
Epoch 189/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4733 - accuracy: 0.7896 - mean_pred: 0.3549
Epoch 190/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4736 - accuracy: 0.7933 - mean_pred: 0.3590
Epoch 191/1000
537/537 [==============================] - 0s 13us/step - loss: 0.4732 - accuracy: 0.7933 - mean_pred: 0.3556
Epoch 192/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4736 - accuracy: 0.7933 - mean_pred: 0.3515
Epoch 193/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4736 - accuracy: 0.7877 - mean_pred: 0.3528
Epoch 194/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4718 - accuracy: 0.7933 - mean_pred: 0.3617
Epoch 195/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4724 - accuracy: 0.7933 - mean_pred: 0.3609
Epoch 196/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4712 - accuracy: 0.7970 - mean_pred: 0.3480
Epoch 197/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4737 - accuracy: 0.7933 - mean_pred: 0.3596
Epoch 198/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4724 - accuracy: 0.7952 - mean_pred: 0.3517
Epoch 199/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4716 - accuracy: 0.7933 - mean_pred: 0.3630
Epoch 200/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4714 - accuracy: 0.7914 - mean_pred: 0.3499
Epoch 201/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4720 - accuracy: 0.7989 - mean_pred: 0.3575
Epoch 202/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4702 - accuracy: 0.8026 - mean_pred: 0.3572
Epoch 203/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4711 - accuracy: 0.7970 - mean_pred: 0.3523
Epoch 204/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4707 - accuracy: 0.7989 - mean_pred: 0.3604
Epoch 205/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4716 - accuracy: 0.7952 - mean_pred: 0.3544
Epoch 206/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4702 - accuracy: 0.7970 - mean_pred: 0.3564
Epoch 207/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4704 - accuracy: 0.7952 - mean_pred: 0.3595
Epoch 208/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4700 - accuracy: 0.7970 - mean_pred: 0.3539
Epoch 209/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4698 - accuracy: 0.7970 - mean_pred: 0.3592
Epoch 210/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4699 - accuracy: 0.7989 - mean_pred: 0.3521
Epoch 211/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4705 - accuracy: 0.7970 - mean_pred: 0.3609
Epoch 212/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4689 - accuracy: 0.8026 - mean_pred: 0.3549
Epoch 213/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4685 - accuracy: 0.7989 - mean_pred: 0.3577
Epoch 214/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4696 - accuracy: 0.7933 - mean_pred: 0.3598
Epoch 215/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4688 - accuracy: 0.7933 - mean_pred: 0.3560
Epoch 216/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4690 - accuracy: 0.7989 - mean_pred: 0.3558
Epoch 217/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4689 - accuracy: 0.8007 - mean_pred: 0.3592
Epoch 218/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4691 - accuracy: 0.7896 - mean_pred: 0.3568
Epoch 219/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4677 - accuracy: 0.7952 - mean_pred: 0.3503
Epoch 220/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4679 - accuracy: 0.7933 - mean_pred: 0.3649
Epoch 221/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4678 - accuracy: 0.7952 - mean_pred: 0.3500
Epoch 222/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4670 - accuracy: 0.7933 - mean_pred: 0.3630
Epoch 223/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4684 - accuracy: 0.7970 - mean_pred: 0.3514
Epoch 224/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4670 - accuracy: 0.7877 - mean_pred: 0.3637
Epoch 225/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4672 - accuracy: 0.7989 - mean_pred: 0.3617
Epoch 226/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4671 - accuracy: 0.7970 - mean_pred: 0.3563
Epoch 227/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4668 - accuracy: 0.8045 - mean_pred: 0.3604
Epoch 228/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4673 - accuracy: 0.7970 - mean_pred: 0.3507
Epoch 229/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4667 - accuracy: 0.7952 - mean_pred: 0.3589
Epoch 230/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4653 - accuracy: 0.7914 - mean_pred: 0.3641
Epoch 231/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4663 - accuracy: 0.8045 - mean_pred: 0.3469
Epoch 232/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4662 - accuracy: 0.7877 - mean_pred: 0.3721
Epoch 233/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4662 - accuracy: 0.8007 - mean_pred: 0.3522
Epoch 234/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4661 - accuracy: 0.8026 - mean_pred: 0.3560
Epoch 235/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4659 - accuracy: 0.7970 - mean_pred: 0.3556
Epoch 236/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4655 - accuracy: 0.7933 - mean_pred: 0.3549
Epoch 237/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4661 - accuracy: 0.8007 - mean_pred: 0.3633
Epoch 238/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4653 - accuracy: 0.7952 - mean_pred: 0.3530
Epoch 239/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4657 - accuracy: 0.8007 - mean_pred: 0.3520
Epoch 240/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4643 - accuracy: 0.7933 - mean_pred: 0.3635
Epoch 241/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4652 - accuracy: 0.7989 - mean_pred: 0.3472
Epoch 242/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4653 - accuracy: 0.7952 - mean_pred: 0.3667
Epoch 243/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4667 - accuracy: 0.7933 - mean_pred: 0.3625
Epoch 244/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4641 - accuracy: 0.7989 - mean_pred: 0.3563
Epoch 245/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4640 - accuracy: 0.7970 - mean_pred: 0.3565
Epoch 246/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4640 - accuracy: 0.7970 - mean_pred: 0.3497
Epoch 247/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4639 - accuracy: 0.7970 - mean_pred: 0.3587
Epoch 248/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4644 - accuracy: 0.7952 - mean_pred: 0.3604
Epoch 249/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4636 - accuracy: 0.8026 - mean_pred: 0.3540
Epoch 250/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4625 - accuracy: 0.7989 - mean_pred: 0.3627
Epoch 251/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4634 - accuracy: 0.7989 - mean_pred: 0.3522
Epoch 252/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4630 - accuracy: 0.8045 - mean_pred: 0.3538
Epoch 253/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4649 - accuracy: 0.8007 - mean_pred: 0.3622
Epoch 254/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4616 - accuracy: 0.8026 - mean_pred: 0.3586
Epoch 255/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4653 - accuracy: 0.7970 - mean_pred: 0.3586
Epoch 256/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4610 - accuracy: 0.8045 - mean_pred: 0.3577
Epoch 257/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4623 - accuracy: 0.7989 - mean_pred: 0.3538
Epoch 258/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4638 - accuracy: 0.7914 - mean_pred: 0.3683
Epoch 259/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4620 - accuracy: 0.7989 - mean_pred: 0.3489
Epoch 260/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4626 - accuracy: 0.8026 - mean_pred: 0.3545
Epoch 261/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4613 - accuracy: 0.8045 - mean_pred: 0.3548
Epoch 262/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4618 - accuracy: 0.7970 - mean_pred: 0.3657
Epoch 263/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4627 - accuracy: 0.8026 - mean_pred: 0.3516
Epoch 264/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4605 - accuracy: 0.8007 - mean_pred: 0.3630
Epoch 265/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4610 - accuracy: 0.8026 - mean_pred: 0.3587
Epoch 266/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4609 - accuracy: 0.8007 - mean_pred: 0.3519
Epoch 267/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4594 - accuracy: 0.8007 - mean_pred: 0.3638
Epoch 268/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4625 - accuracy: 0.8026 - mean_pred: 0.3529
Epoch 269/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4421 - accuracy: 0.8045 - mean_pred: 0.3587
Epoch 270/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4221 - accuracy: 0.8026 - mean_pred: 0.3173
Epoch 271/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4181 - accuracy: 0.8045 - mean_pred: 0.3159
Epoch 272/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4163 - accuracy: 0.8007 - mean_pred: 0.3300
Epoch 273/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4170 - accuracy: 0.7952 - mean_pred: 0.3381
Epoch 274/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4154 - accuracy: 0.7989 - mean_pred: 0.3378
Epoch 275/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4179 - accuracy: 0.8026 - mean_pred: 0.3443
Epoch 276/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4164 - accuracy: 0.7989 - mean_pred: 0.3381
Epoch 277/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4153 - accuracy: 0.7952 - mean_pred: 0.3425
Epoch 278/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4152 - accuracy: 0.7933 - mean_pred: 0.3421
Epoch 279/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4144 - accuracy: 0.8007 - mean_pred: 0.3400
Epoch 280/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4155 - accuracy: 0.8007 - mean_pred: 0.3415
Epoch 281/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4147 - accuracy: 0.8063 - mean_pred: 0.3537
Epoch 282/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4154 - accuracy: 0.7989 - mean_pred: 0.3435
Epoch 283/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4132 - accuracy: 0.8007 - mean_pred: 0.3436
Epoch 284/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4138 - accuracy: 0.8045 - mean_pred: 0.3499
Epoch 285/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4138 - accuracy: 0.8007 - mean_pred: 0.3407
Epoch 286/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4148 - accuracy: 0.7933 - mean_pred: 0.3358
Epoch 287/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4135 - accuracy: 0.8063 - mean_pred: 0.3528
Epoch 288/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4130 - accuracy: 0.8026 - mean_pred: 0.3462
Epoch 289/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4136 - accuracy: 0.7952 - mean_pred: 0.3485
Epoch 290/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4136 - accuracy: 0.8082 - mean_pred: 0.3492
Epoch 291/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4124 - accuracy: 0.8063 - mean_pred: 0.3506
Epoch 292/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4126 - accuracy: 0.8045 - mean_pred: 0.3428
Epoch 293/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4145 - accuracy: 0.8026 - mean_pred: 0.3541
Epoch 294/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4117 - accuracy: 0.8026 - mean_pred: 0.3440
Epoch 295/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4121 - accuracy: 0.8063 - mean_pred: 0.3456
Epoch 296/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4112 - accuracy: 0.8082 - mean_pred: 0.3489
Epoch 297/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4136 - accuracy: 0.8045 - mean_pred: 0.3568
Epoch 298/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4118 - accuracy: 0.8082 - mean_pred: 0.3507
Epoch 299/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4111 - accuracy: 0.7989 - mean_pred: 0.3540
Epoch 300/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4116 - accuracy: 0.8063 - mean_pred: 0.3544
Epoch 301/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4124 - accuracy: 0.7914 - mean_pred: 0.3499
Epoch 302/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4112 - accuracy: 0.8026 - mean_pred: 0.3405
Epoch 303/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4101 - accuracy: 0.8045 - mean_pred: 0.3417
Epoch 304/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4112 - accuracy: 0.8026 - mean_pred: 0.3578
Epoch 305/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4140 - accuracy: 0.8007 - mean_pred: 0.3515
Epoch 306/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4100 - accuracy: 0.8045 - mean_pred: 0.3506
Epoch 307/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4109 - accuracy: 0.8007 - mean_pred: 0.3524
Epoch 308/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4102 - accuracy: 0.8045 - mean_pred: 0.3523
Epoch 309/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4093 - accuracy: 0.8082 - mean_pred: 0.3534
Epoch 310/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4104 - accuracy: 0.8007 - mean_pred: 0.3495
Epoch 311/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4123 - accuracy: 0.8045 - mean_pred: 0.3470
Epoch 312/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4103 - accuracy: 0.8045 - mean_pred: 0.3493
Epoch 313/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4100 - accuracy: 0.8026 - mean_pred: 0.3576
Epoch 314/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4101 - accuracy: 0.8063 - mean_pred: 0.3497
Epoch 315/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4097 - accuracy: 0.8082 - mean_pred: 0.3456
Epoch 316/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4102 - accuracy: 0.8007 - mean_pred: 0.3520
Epoch 317/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4106 - accuracy: 0.7989 - mean_pred: 0.3514
Epoch 318/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4086 - accuracy: 0.8007 - mean_pred: 0.3534
Epoch 319/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4103 - accuracy: 0.8026 - mean_pred: 0.3500
Epoch 320/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4126 - accuracy: 0.8063 - mean_pred: 0.3619
Epoch 321/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4080 - accuracy: 0.8063 - mean_pred: 0.3399
Epoch 322/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4090 - accuracy: 0.8007 - mean_pred: 0.3620
Epoch 323/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4094 - accuracy: 0.8063 - mean_pred: 0.3479
Epoch 324/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4087 - accuracy: 0.8101 - mean_pred: 0.3521
Epoch 325/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4093 - accuracy: 0.7970 - mean_pred: 0.3603
Epoch 326/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4089 - accuracy: 0.8082 - mean_pred: 0.3466
Epoch 327/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4110 - accuracy: 0.8026 - mean_pred: 0.3657
Epoch 328/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4090 - accuracy: 0.8063 - mean_pred: 0.3471
Epoch 329/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4095 - accuracy: 0.8026 - mean_pred: 0.3559
Epoch 330/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4087 - accuracy: 0.8007 - mean_pred: 0.3521
Epoch 331/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4091 - accuracy: 0.7970 - mean_pred: 0.3483
Epoch 332/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4071 - accuracy: 0.8082 - mean_pred: 0.3615
Epoch 333/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4105 - accuracy: 0.8138 - mean_pred: 0.3459
Epoch 334/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4099 - accuracy: 0.8007 - mean_pred: 0.3418
Epoch 335/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4090 - accuracy: 0.8045 - mean_pred: 0.3470
Epoch 336/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4090 - accuracy: 0.8119 - mean_pred: 0.3592
Epoch 337/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4076 - accuracy: 0.8045 - mean_pred: 0.3529
Epoch 338/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4090 - accuracy: 0.8063 - mean_pred: 0.3501
Epoch 339/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4103 - accuracy: 0.8119 - mean_pred: 0.3590
Epoch 340/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4071 - accuracy: 0.8026 - mean_pred: 0.3477
Epoch 341/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4089 - accuracy: 0.8026 - mean_pred: 0.3524
Epoch 342/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4083 - accuracy: 0.8063 - mean_pred: 0.3547
Epoch 343/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4076 - accuracy: 0.8063 - mean_pred: 0.3508
Epoch 344/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4079 - accuracy: 0.8045 - mean_pred: 0.3519
Epoch 345/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4100 - accuracy: 0.8045 - mean_pred: 0.3518
Epoch 346/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4062 - accuracy: 0.8045 - mean_pred: 0.3503
Epoch 347/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4080 - accuracy: 0.8026 - mean_pred: 0.3577
Epoch 348/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4077 - accuracy: 0.8045 - mean_pred: 0.3484
Epoch 349/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4079 - accuracy: 0.8063 - mean_pred: 0.3452
Epoch 350/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4086 - accuracy: 0.8007 - mean_pred: 0.3546
Epoch 351/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4070 - accuracy: 0.8063 - mean_pred: 0.3506
Epoch 352/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4079 - accuracy: 0.8063 - mean_pred: 0.3477
Epoch 353/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4077 - accuracy: 0.7989 - mean_pred: 0.3578
Epoch 354/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4072 - accuracy: 0.8026 - mean_pred: 0.3521
Epoch 355/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4064 - accuracy: 0.8101 - mean_pred: 0.3546
Epoch 356/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4086 - accuracy: 0.8101 - mean_pred: 0.3557
Epoch 357/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4073 - accuracy: 0.8138 - mean_pred: 0.3554
Epoch 358/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4054 - accuracy: 0.8063 - mean_pred: 0.3603
Epoch 359/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4046 - accuracy: 0.8007 - mean_pred: 0.3400
Epoch 360/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4077 - accuracy: 0.8045 - mean_pred: 0.3688
Epoch 361/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4072 - accuracy: 0.8082 - mean_pred: 0.3637
Epoch 362/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4063 - accuracy: 0.8082 - mean_pred: 0.3479
Epoch 363/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4056 - accuracy: 0.8045 - mean_pred: 0.3500
Epoch 364/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4071 - accuracy: 0.8063 - mean_pred: 0.3550
Epoch 365/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4096 - accuracy: 0.8063 - mean_pred: 0.3521
Epoch 366/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4058 - accuracy: 0.8007 - mean_pred: 0.3558
Epoch 367/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4071 - accuracy: 0.8007 - mean_pred: 0.3529
Epoch 368/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4054 - accuracy: 0.8119 - mean_pred: 0.3462
Epoch 369/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4067 - accuracy: 0.8063 - mean_pred: 0.3597
Epoch 370/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4057 - accuracy: 0.8101 - mean_pred: 0.3581
Epoch 371/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4067 - accuracy: 0.8026 - mean_pred: 0.3465
Epoch 372/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4063 - accuracy: 0.8026 - mean_pred: 0.3563
Epoch 373/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4061 - accuracy: 0.8101 - mean_pred: 0.3516
Epoch 374/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4080 - accuracy: 0.8082 - mean_pred: 0.3454
Epoch 375/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4061 - accuracy: 0.8101 - mean_pred: 0.3567
Epoch 376/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4062 - accuracy: 0.8045 - mean_pred: 0.3595
Epoch 377/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4066 - accuracy: 0.8119 - mean_pred: 0.3496
Epoch 378/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4057 - accuracy: 0.8007 - mean_pred: 0.3537
Epoch 379/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4064 - accuracy: 0.8045 - mean_pred: 0.3531
Epoch 380/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4050 - accuracy: 0.8063 - mean_pred: 0.3550
Epoch 381/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4055 - accuracy: 0.8101 - mean_pred: 0.3600
Epoch 382/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4059 - accuracy: 0.8119 - mean_pred: 0.3507
Epoch 383/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4068 - accuracy: 0.8101 - mean_pred: 0.3512
Epoch 384/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4040 - accuracy: 0.8082 - mean_pred: 0.3629
Epoch 385/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4066 - accuracy: 0.8063 - mean_pred: 0.3487
Epoch 386/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4055 - accuracy: 0.8082 - mean_pred: 0.3488
Epoch 387/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4054 - accuracy: 0.8045 - mean_pred: 0.3591
Epoch 388/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4052 - accuracy: 0.8101 - mean_pred: 0.3522
Epoch 389/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4053 - accuracy: 0.8045 - mean_pred: 0.3549
Epoch 390/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4049 - accuracy: 0.8082 - mean_pred: 0.3598
Epoch 391/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4044 - accuracy: 0.8101 - mean_pred: 0.3480
Epoch 392/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4053 - accuracy: 0.8082 - mean_pred: 0.3607
Epoch 393/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4022 - accuracy: 0.8045 - mean_pred: 0.3474
Epoch 394/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4059 - accuracy: 0.8045 - mean_pred: 0.3658
Epoch 395/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4053 - accuracy: 0.8026 - mean_pred: 0.3547
Epoch 396/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4049 - accuracy: 0.8007 - mean_pred: 0.3490
Epoch 397/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4059 - accuracy: 0.8045 - mean_pred: 0.3565
Epoch 398/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4050 - accuracy: 0.7989 - mean_pred: 0.3529
Epoch 399/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4048 - accuracy: 0.8063 - mean_pred: 0.3650
Epoch 400/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4052 - accuracy: 0.8063 - mean_pred: 0.3537
Epoch 401/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4042 - accuracy: 0.8063 - mean_pred: 0.3514
Epoch 402/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4040 - accuracy: 0.8007 - mean_pred: 0.3554
Epoch 403/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4052 - accuracy: 0.8082 - mean_pred: 0.3566
Epoch 404/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4042 - accuracy: 0.8082 - mean_pred: 0.3452
Epoch 405/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4037 - accuracy: 0.8026 - mean_pred: 0.3585
Epoch 406/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4042 - accuracy: 0.8045 - mean_pred: 0.3598
Epoch 407/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4054 - accuracy: 0.8007 - mean_pred: 0.3459
Epoch 408/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4039 - accuracy: 0.8082 - mean_pred: 0.3624
Epoch 409/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4031 - accuracy: 0.8026 - mean_pred: 0.3494
Epoch 410/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4045 - accuracy: 0.8101 - mean_pred: 0.3616
Epoch 411/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4050 - accuracy: 0.8063 - mean_pred: 0.3520
Epoch 412/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4029 - accuracy: 0.8045 - mean_pred: 0.3510
Epoch 413/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4046 - accuracy: 0.8063 - mean_pred: 0.3597
Epoch 414/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4029 - accuracy: 0.8063 - mean_pred: 0.3551
Epoch 415/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4022 - accuracy: 0.8045 - mean_pred: 0.3648
Epoch 416/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4042 - accuracy: 0.8101 - mean_pred: 0.3446
Epoch 417/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4044 - accuracy: 0.8007 - mean_pred: 0.3554
Epoch 418/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4031 - accuracy: 0.8101 - mean_pred: 0.3560
Epoch 419/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4041 - accuracy: 0.8101 - mean_pred: 0.3586
Epoch 420/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4035 - accuracy: 0.8101 - mean_pred: 0.3568
Epoch 421/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4023 - accuracy: 0.8101 - mean_pred: 0.3525
Epoch 422/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4057 - accuracy: 0.8045 - mean_pred: 0.3633
Epoch 423/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4034 - accuracy: 0.8119 - mean_pred: 0.3555
Epoch 424/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4028 - accuracy: 0.8045 - mean_pred: 0.3551
Epoch 425/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4030 - accuracy: 0.8063 - mean_pred: 0.3556
Epoch 426/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4046 - accuracy: 0.8082 - mean_pred: 0.3552
Epoch 427/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4039 - accuracy: 0.8101 - mean_pred: 0.3568
Epoch 428/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4034 - accuracy: 0.8063 - mean_pred: 0.3554
Epoch 429/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4031 - accuracy: 0.8063 - mean_pred: 0.3550
Epoch 430/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4030 - accuracy: 0.8007 - mean_pred: 0.3550
Epoch 431/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4048 - accuracy: 0.8026 - mean_pred: 0.3550
Epoch 432/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4012 - accuracy: 0.8082 - mean_pred: 0.3546
Epoch 433/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4022 - accuracy: 0.8007 - mean_pred: 0.3589
Epoch 434/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4027 - accuracy: 0.8007 - mean_pred: 0.3582
Epoch 435/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4030 - accuracy: 0.8063 - mean_pred: 0.3596
Epoch 436/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4026 - accuracy: 0.8063 - mean_pred: 0.3444
Epoch 437/1000
537/537 [==============================] - ETA: 0s - loss: 0.6637 - accuracy: 0.8200 - mean_pred: 0.31 - 0s 15us/step - loss: 0.4030 - accuracy: 0.8007 - mean_pred: 0.3570
Epoch 438/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4032 - accuracy: 0.8063 - mean_pred: 0.3557
Epoch 439/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4006 - accuracy: 0.8101 - mean_pred: 0.3665
Epoch 440/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4024 - accuracy: 0.8119 - mean_pred: 0.3536
Epoch 441/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4031 - accuracy: 0.8082 - mean_pred: 0.3649
Epoch 442/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4016 - accuracy: 0.8063 - mean_pred: 0.3528
Epoch 443/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4012 - accuracy: 0.8007 - mean_pred: 0.3471
Epoch 444/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4025 - accuracy: 0.8045 - mean_pred: 0.3635
Epoch 445/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4032 - accuracy: 0.8045 - mean_pred: 0.3598
Epoch 446/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4035 - accuracy: 0.8026 - mean_pred: 0.3614
Epoch 447/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4024 - accuracy: 0.8045 - mean_pred: 0.3514
Epoch 448/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4017 - accuracy: 0.8026 - mean_pred: 0.3569
Epoch 449/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4018 - accuracy: 0.8045 - mean_pred: 0.3533
Epoch 450/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4013 - accuracy: 0.8101 - mean_pred: 0.3554
Epoch 451/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4021 - accuracy: 0.8138 - mean_pred: 0.3533
Epoch 452/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4029 - accuracy: 0.8045 - mean_pred: 0.3669
Epoch 453/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4016 - accuracy: 0.8082 - mean_pred: 0.3536
Epoch 454/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4012 - accuracy: 0.8101 - mean_pred: 0.3574
Epoch 455/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4018 - accuracy: 0.8101 - mean_pred: 0.3534
Epoch 456/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4008 - accuracy: 0.8007 - mean_pred: 0.3635
Epoch 457/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4041 - accuracy: 0.8101 - mean_pred: 0.3566
Epoch 458/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3991 - accuracy: 0.7989 - mean_pred: 0.3509
Epoch 459/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3992 - accuracy: 0.8045 - mean_pred: 0.3501
Epoch 460/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3999 - accuracy: 0.8082 - mean_pred: 0.3537
Epoch 461/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3999 - accuracy: 0.8045 - mean_pred: 0.3608
Epoch 462/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4011 - accuracy: 0.8045 - mean_pred: 0.3509
Epoch 463/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4014 - accuracy: 0.8063 - mean_pred: 0.3560
Epoch 464/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4004 - accuracy: 0.8119 - mean_pred: 0.3565
Epoch 465/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4018 - accuracy: 0.8045 - mean_pred: 0.3532
Epoch 466/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4010 - accuracy: 0.8045 - mean_pred: 0.3549
Epoch 467/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4039 - accuracy: 0.8026 - mean_pred: 0.3511
Epoch 468/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3996 - accuracy: 0.8026 - mean_pred: 0.3596
Epoch 469/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3995 - accuracy: 0.8063 - mean_pred: 0.3498
Epoch 470/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4008 - accuracy: 0.7989 - mean_pred: 0.3617
Epoch 471/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4002 - accuracy: 0.8045 - mean_pred: 0.3536
Epoch 472/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4020 - accuracy: 0.7989 - mean_pred: 0.3562
Epoch 473/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4009 - accuracy: 0.8082 - mean_pred: 0.3505
Epoch 474/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3996 - accuracy: 0.8119 - mean_pred: 0.3575
Epoch 475/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4015 - accuracy: 0.8007 - mean_pred: 0.3466
Epoch 476/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4033 - accuracy: 0.8082 - mean_pred: 0.3635
Epoch 477/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3994 - accuracy: 0.8063 - mean_pred: 0.3551
Epoch 478/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4009 - accuracy: 0.8082 - mean_pred: 0.3506
Epoch 479/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3999 - accuracy: 0.8063 - mean_pred: 0.3607
Epoch 480/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4000 - accuracy: 0.8063 - mean_pred: 0.3459
Epoch 481/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4003 - accuracy: 0.8082 - mean_pred: 0.3591
Epoch 482/1000
537/537 [==============================] - 0s 19us/step - loss: 0.4053 - accuracy: 0.8101 - mean_pred: 0.3593
Epoch 483/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3978 - accuracy: 0.8026 - mean_pred: 0.3516
Epoch 484/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3979 - accuracy: 0.8045 - mean_pred: 0.3533
Epoch 485/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3984 - accuracy: 0.8026 - mean_pred: 0.3543
Epoch 486/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3985 - accuracy: 0.8045 - mean_pred: 0.3530
Epoch 487/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4000 - accuracy: 0.8063 - mean_pred: 0.3595
Epoch 488/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3998 - accuracy: 0.8045 - mean_pred: 0.3530
Epoch 489/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4002 - accuracy: 0.8063 - mean_pred: 0.3562
Epoch 490/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3995 - accuracy: 0.8063 - mean_pred: 0.3542
Epoch 491/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4002 - accuracy: 0.8026 - mean_pred: 0.3636
Epoch 492/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4000 - accuracy: 0.8045 - mean_pred: 0.3526
Epoch 493/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4015 - accuracy: 0.8119 - mean_pred: 0.3582
Epoch 494/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3998 - accuracy: 0.8082 - mean_pred: 0.3497
Epoch 495/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3996 - accuracy: 0.8082 - mean_pred: 0.3589
Epoch 496/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3990 - accuracy: 0.7970 - mean_pred: 0.3478
Epoch 497/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3983 - accuracy: 0.8045 - mean_pred: 0.3637
Epoch 498/1000
537/537 [==============================] - 0s 15us/step - loss: 0.4014 - accuracy: 0.8063 - mean_pred: 0.3603
Epoch 499/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3993 - accuracy: 0.8082 - mean_pred: 0.3506
Epoch 500/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3997 - accuracy: 0.8082 - mean_pred: 0.3406
Epoch 501/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3998 - accuracy: 0.8045 - mean_pred: 0.3638
Epoch 502/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3990 - accuracy: 0.8026 - mean_pred: 0.3464
Epoch 503/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3993 - accuracy: 0.8063 - mean_pred: 0.3536
Epoch 504/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3994 - accuracy: 0.8026 - mean_pred: 0.3574
Epoch 505/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4003 - accuracy: 0.8007 - mean_pred: 0.3601
Epoch 506/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3982 - accuracy: 0.8063 - mean_pred: 0.3549
Epoch 507/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3983 - accuracy: 0.8026 - mean_pred: 0.3568
Epoch 508/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3992 - accuracy: 0.8119 - mean_pred: 0.3594
Epoch 509/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3989 - accuracy: 0.8045 - mean_pred: 0.3507
Epoch 510/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4008 - accuracy: 0.8026 - mean_pred: 0.3566
Epoch 511/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3995 - accuracy: 0.8045 - mean_pred: 0.3573
Epoch 512/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3984 - accuracy: 0.8007 - mean_pred: 0.3569
Epoch 513/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3980 - accuracy: 0.8026 - mean_pred: 0.3557
Epoch 514/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3990 - accuracy: 0.7952 - mean_pred: 0.3502
Epoch 515/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3974 - accuracy: 0.8063 - mean_pred: 0.3630
Epoch 516/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3983 - accuracy: 0.7989 - mean_pred: 0.3549
Epoch 517/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3981 - accuracy: 0.8026 - mean_pred: 0.3513
Epoch 518/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3985 - accuracy: 0.8045 - mean_pred: 0.3590
Epoch 519/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3986 - accuracy: 0.8082 - mean_pred: 0.3626
Epoch 520/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3973 - accuracy: 0.8045 - mean_pred: 0.3467
Epoch 521/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3996 - accuracy: 0.8063 - mean_pred: 0.3606
Epoch 522/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3976 - accuracy: 0.8007 - mean_pred: 0.3579
Epoch 523/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3995 - accuracy: 0.8063 - mean_pred: 0.3522
Epoch 524/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3974 - accuracy: 0.8026 - mean_pred: 0.3483
Epoch 525/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3990 - accuracy: 0.8007 - mean_pred: 0.3656
Epoch 526/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3974 - accuracy: 0.8026 - mean_pred: 0.3476
Epoch 527/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3967 - accuracy: 0.8063 - mean_pred: 0.3539
Epoch 528/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3965 - accuracy: 0.8045 - mean_pred: 0.3551
Epoch 529/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3988 - accuracy: 0.8063 - mean_pred: 0.3600
Epoch 530/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3973 - accuracy: 0.8026 - mean_pred: 0.3584
Epoch 531/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3966 - accuracy: 0.8156 - mean_pred: 0.3676
Epoch 532/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3978 - accuracy: 0.8026 - mean_pred: 0.3446
Epoch 533/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3958 - accuracy: 0.8082 - mean_pred: 0.3562
Epoch 534/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3965 - accuracy: 0.8101 - mean_pred: 0.3615
Epoch 535/1000
537/537 [==============================] - 0s 17us/step - loss: 0.4002 - accuracy: 0.7989 - mean_pred: 0.3583
Epoch 536/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3963 - accuracy: 0.8101 - mean_pred: 0.3494
Epoch 537/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3963 - accuracy: 0.8045 - mean_pred: 0.3615
Epoch 538/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3958 - accuracy: 0.8026 - mean_pred: 0.3542
Epoch 539/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3973 - accuracy: 0.8082 - mean_pred: 0.3571
Epoch 540/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3970 - accuracy: 0.8045 - mean_pred: 0.3610
Epoch 541/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3978 - accuracy: 0.8026 - mean_pred: 0.3550
Epoch 542/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3964 - accuracy: 0.7989 - mean_pred: 0.3521
Epoch 543/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3967 - accuracy: 0.7989 - mean_pred: 0.3592
Epoch 544/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3966 - accuracy: 0.8082 - mean_pred: 0.3610
Epoch 545/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3962 - accuracy: 0.8045 - mean_pred: 0.3513
Epoch 546/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3953 - accuracy: 0.8026 - mean_pred: 0.3491
Epoch 547/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3978 - accuracy: 0.8026 - mean_pred: 0.3721
Epoch 548/1000
537/537 [==============================] - 0s 13us/step - loss: 0.3953 - accuracy: 0.8026 - mean_pred: 0.3611
Epoch 549/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3969 - accuracy: 0.8045 - mean_pred: 0.3504
Epoch 550/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3972 - accuracy: 0.7989 - mean_pred: 0.3522
Epoch 551/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3962 - accuracy: 0.8063 - mean_pred: 0.3581
Epoch 552/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3955 - accuracy: 0.8026 - mean_pred: 0.3492
Epoch 553/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3956 - accuracy: 0.8026 - mean_pred: 0.3601
Epoch 554/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3963 - accuracy: 0.7914 - mean_pred: 0.3551
Epoch 555/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3944 - accuracy: 0.8063 - mean_pred: 0.3548
Epoch 556/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3964 - accuracy: 0.8007 - mean_pred: 0.3660
Epoch 557/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3940 - accuracy: 0.8101 - mean_pred: 0.3469
Epoch 558/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3971 - accuracy: 0.8045 - mean_pred: 0.3667
Epoch 559/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3952 - accuracy: 0.7989 - mean_pred: 0.3610
Epoch 560/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3970 - accuracy: 0.8045 - mean_pred: 0.3525
Epoch 561/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3961 - accuracy: 0.8007 - mean_pred: 0.3537
Epoch 562/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3951 - accuracy: 0.7989 - mean_pred: 0.3623
Epoch 563/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3955 - accuracy: 0.7989 - mean_pred: 0.3562
Epoch 564/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3970 - accuracy: 0.8007 - mean_pred: 0.3579
Epoch 565/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3960 - accuracy: 0.7952 - mean_pred: 0.3536
Epoch 566/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3948 - accuracy: 0.8026 - mean_pred: 0.3596
Epoch 567/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3972 - accuracy: 0.8007 - mean_pred: 0.3578
Epoch 568/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3950 - accuracy: 0.7952 - mean_pred: 0.3583
Epoch 569/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3958 - accuracy: 0.8007 - mean_pred: 0.3555
Epoch 570/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3970 - accuracy: 0.8026 - mean_pred: 0.3636
Epoch 571/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3957 - accuracy: 0.8007 - mean_pred: 0.3585
Epoch 572/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3945 - accuracy: 0.8101 - mean_pred: 0.3492
Epoch 573/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3944 - accuracy: 0.8007 - mean_pred: 0.3600
Epoch 574/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3945 - accuracy: 0.8007 - mean_pred: 0.3549
Epoch 575/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3944 - accuracy: 0.7989 - mean_pred: 0.3510
Epoch 576/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3955 - accuracy: 0.8007 - mean_pred: 0.3608
Epoch 577/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3947 - accuracy: 0.7952 - mean_pred: 0.3643
Epoch 578/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3968 - accuracy: 0.7970 - mean_pred: 0.3514
Epoch 579/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3934 - accuracy: 0.7970 - mean_pred: 0.3672
Epoch 580/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3937 - accuracy: 0.8007 - mean_pred: 0.3509
Epoch 581/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3947 - accuracy: 0.8045 - mean_pred: 0.3591
Epoch 582/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3933 - accuracy: 0.8026 - mean_pred: 0.3592
Epoch 583/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3926 - accuracy: 0.7989 - mean_pred: 0.3549
Epoch 584/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3953 - accuracy: 0.7952 - mean_pred: 0.3582
Epoch 585/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3961 - accuracy: 0.7970 - mean_pred: 0.3652
Epoch 586/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3940 - accuracy: 0.7952 - mean_pred: 0.3561
Epoch 587/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3941 - accuracy: 0.7970 - mean_pred: 0.3497
Epoch 588/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3931 - accuracy: 0.7989 - mean_pred: 0.3559
Epoch 589/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3938 - accuracy: 0.7970 - mean_pred: 0.3607
Epoch 590/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3934 - accuracy: 0.8045 - mean_pred: 0.3525
Epoch 591/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3937 - accuracy: 0.7933 - mean_pred: 0.3683
Epoch 592/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3935 - accuracy: 0.7952 - mean_pred: 0.3561
Epoch 593/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3926 - accuracy: 0.7989 - mean_pred: 0.3530
Epoch 594/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3952 - accuracy: 0.7933 - mean_pred: 0.3582
Epoch 595/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3941 - accuracy: 0.7933 - mean_pred: 0.3627
Epoch 596/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3935 - accuracy: 0.7970 - mean_pred: 0.3663
Epoch 597/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3938 - accuracy: 0.7952 - mean_pred: 0.3502
Epoch 598/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3936 - accuracy: 0.7970 - mean_pred: 0.3585
Epoch 599/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3922 - accuracy: 0.7970 - mean_pred: 0.3643
Epoch 600/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3924 - accuracy: 0.7952 - mean_pred: 0.3560
Epoch 601/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3921 - accuracy: 0.7952 - mean_pred: 0.3540
Epoch 602/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3921 - accuracy: 0.7933 - mean_pred: 0.3569
Epoch 603/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3937 - accuracy: 0.7914 - mean_pred: 0.3588
Epoch 604/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3930 - accuracy: 0.7952 - mean_pred: 0.3610
Epoch 605/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3913 - accuracy: 0.7914 - mean_pred: 0.3574
Epoch 606/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3913 - accuracy: 0.8063 - mean_pred: 0.3531
Epoch 607/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3917 - accuracy: 0.7952 - mean_pred: 0.3563
Epoch 608/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3939 - accuracy: 0.7952 - mean_pred: 0.3590
Epoch 609/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3906 - accuracy: 0.7933 - mean_pred: 0.3570
Epoch 610/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3909 - accuracy: 0.7952 - mean_pred: 0.3570
Epoch 611/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3932 - accuracy: 0.7952 - mean_pred: 0.3585
Epoch 612/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3902 - accuracy: 0.7989 - mean_pred: 0.3526
Epoch 613/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3905 - accuracy: 0.7952 - mean_pred: 0.3526
Epoch 614/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3933 - accuracy: 0.7877 - mean_pred: 0.3639
Epoch 615/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3907 - accuracy: 0.7952 - mean_pred: 0.3581
Epoch 616/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3921 - accuracy: 0.8007 - mean_pred: 0.3565
Epoch 617/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3921 - accuracy: 0.7914 - mean_pred: 0.3584
Epoch 618/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3903 - accuracy: 0.7952 - mean_pred: 0.3608
Epoch 619/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3900 - accuracy: 0.7970 - mean_pred: 0.3535
Epoch 620/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3906 - accuracy: 0.7933 - mean_pred: 0.3688
Epoch 621/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3912 - accuracy: 0.7989 - mean_pred: 0.3544
Epoch 622/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3903 - accuracy: 0.7914 - mean_pred: 0.3573
Epoch 623/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3898 - accuracy: 0.7989 - mean_pred: 0.3563
Epoch 624/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3903 - accuracy: 0.7858 - mean_pred: 0.3579
Epoch 625/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3922 - accuracy: 0.7914 - mean_pred: 0.3571
Epoch 626/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3913 - accuracy: 0.7877 - mean_pred: 0.3575
Epoch 627/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3923 - accuracy: 0.7970 - mean_pred: 0.3563
Epoch 628/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3889 - accuracy: 0.7914 - mean_pred: 0.3578
Epoch 629/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3910 - accuracy: 0.7952 - mean_pred: 0.3592
Epoch 630/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3887 - accuracy: 0.7989 - mean_pred: 0.3555
Epoch 631/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3901 - accuracy: 0.7933 - mean_pred: 0.3577
Epoch 632/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3908 - accuracy: 0.7858 - mean_pred: 0.3565
Epoch 633/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3889 - accuracy: 0.7896 - mean_pred: 0.3647
Epoch 634/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3900 - accuracy: 0.7989 - mean_pred: 0.3546
Epoch 635/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3890 - accuracy: 0.8007 - mean_pred: 0.3491
Epoch 636/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3891 - accuracy: 0.7933 - mean_pred: 0.3640
Epoch 637/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3900 - accuracy: 0.7877 - mean_pred: 0.3536
Epoch 638/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3911 - accuracy: 0.8045 - mean_pred: 0.3611
Epoch 639/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3893 - accuracy: 0.7970 - mean_pred: 0.3563
Epoch 640/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3895 - accuracy: 0.8026 - mean_pred: 0.3670
Epoch 641/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3882 - accuracy: 0.7933 - mean_pred: 0.3608
Epoch 642/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3882 - accuracy: 0.7989 - mean_pred: 0.3481
Epoch 643/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3902 - accuracy: 0.7914 - mean_pred: 0.3630
Epoch 644/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3888 - accuracy: 0.7914 - mean_pred: 0.3570
Epoch 645/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3889 - accuracy: 0.7933 - mean_pred: 0.3552
Epoch 646/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3882 - accuracy: 0.7952 - mean_pred: 0.3613
Epoch 647/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3890 - accuracy: 0.7896 - mean_pred: 0.3557
Epoch 648/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3884 - accuracy: 0.7952 - mean_pred: 0.3569
Epoch 649/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3885 - accuracy: 0.7970 - mean_pred: 0.3594
Epoch 650/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3880 - accuracy: 0.7952 - mean_pred: 0.3586
Epoch 651/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3887 - accuracy: 0.7933 - mean_pred: 0.3603
Epoch 652/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3869 - accuracy: 0.7989 - mean_pred: 0.3592
Epoch 653/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3897 - accuracy: 0.7858 - mean_pred: 0.3618
Epoch 654/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3885 - accuracy: 0.7989 - mean_pred: 0.3517
Epoch 655/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3873 - accuracy: 0.7933 - mean_pred: 0.3657
Epoch 656/1000
537/537 [==============================] - 0s 13us/step - loss: 0.3876 - accuracy: 0.7970 - mean_pred: 0.3467
Epoch 657/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3873 - accuracy: 0.7952 - mean_pred: 0.3645
Epoch 658/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3864 - accuracy: 0.7896 - mean_pred: 0.3584
Epoch 659/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3908 - accuracy: 0.7877 - mean_pred: 0.3505
Epoch 660/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3877 - accuracy: 0.7896 - mean_pred: 0.3581
Epoch 661/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3855 - accuracy: 0.8007 - mean_pred: 0.3523
Epoch 662/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3895 - accuracy: 0.7933 - mean_pred: 0.3634
Epoch 663/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3893 - accuracy: 0.7933 - mean_pred: 0.3621
Epoch 664/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3860 - accuracy: 0.7896 - mean_pred: 0.3651
Epoch 665/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3892 - accuracy: 0.7952 - mean_pred: 0.3586
Epoch 666/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3866 - accuracy: 0.7933 - mean_pred: 0.3540
Epoch 667/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3857 - accuracy: 0.7970 - mean_pred: 0.3668
Epoch 668/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3870 - accuracy: 0.7952 - mean_pred: 0.3508
Epoch 669/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3892 - accuracy: 0.7877 - mean_pred: 0.3654
Epoch 670/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3853 - accuracy: 0.7933 - mean_pred: 0.3553
Epoch 671/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3858 - accuracy: 0.7896 - mean_pred: 0.3571
Epoch 672/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3864 - accuracy: 0.7914 - mean_pred: 0.3577
Epoch 673/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3851 - accuracy: 0.7858 - mean_pred: 0.3559
Epoch 674/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3871 - accuracy: 0.7933 - mean_pred: 0.3581
Epoch 675/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3866 - accuracy: 0.7896 - mean_pred: 0.3614
Epoch 676/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3881 - accuracy: 0.7933 - mean_pred: 0.3587
Epoch 677/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3854 - accuracy: 0.7896 - mean_pred: 0.3592
Epoch 678/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3861 - accuracy: 0.7970 - mean_pred: 0.3533
Epoch 679/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3852 - accuracy: 0.7877 - mean_pred: 0.3635
Epoch 680/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3860 - accuracy: 0.7858 - mean_pred: 0.3544
Epoch 681/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3854 - accuracy: 0.7914 - mean_pred: 0.3612
Epoch 682/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3862 - accuracy: 0.7914 - mean_pred: 0.3492
Epoch 683/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3858 - accuracy: 0.7896 - mean_pred: 0.3638
Epoch 684/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3852 - accuracy: 0.7970 - mean_pred: 0.3593
Epoch 685/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3874 - accuracy: 0.7914 - mean_pred: 0.3610
Epoch 686/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3846 - accuracy: 0.7933 - mean_pred: 0.3586
Epoch 687/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3852 - accuracy: 0.8007 - mean_pred: 0.3522
Epoch 688/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3845 - accuracy: 0.7914 - mean_pred: 0.3568
Epoch 689/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3847 - accuracy: 0.7933 - mean_pred: 0.3606
Epoch 690/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3852 - accuracy: 0.7877 - mean_pred: 0.3612
Epoch 691/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3848 - accuracy: 0.7952 - mean_pred: 0.3573
Epoch 692/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3860 - accuracy: 0.7933 - mean_pred: 0.3578
Epoch 693/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3840 - accuracy: 0.7952 - mean_pred: 0.3580
Epoch 694/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3837 - accuracy: 0.7933 - mean_pred: 0.3615
Epoch 695/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3839 - accuracy: 0.7970 - mean_pred: 0.3515
Epoch 696/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3822 - accuracy: 0.7989 - mean_pred: 0.3703
Epoch 697/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3848 - accuracy: 0.7952 - mean_pred: 0.3569
Epoch 698/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3853 - accuracy: 0.7877 - mean_pred: 0.3549
Epoch 699/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3826 - accuracy: 0.7970 - mean_pred: 0.3693
Epoch 700/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3858 - accuracy: 0.7877 - mean_pred: 0.3539
Epoch 701/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3846 - accuracy: 0.7970 - mean_pred: 0.3547
Epoch 702/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3824 - accuracy: 0.7970 - mean_pred: 0.3603
Epoch 703/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3847 - accuracy: 0.7952 - mean_pred: 0.3504
Epoch 704/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3813 - accuracy: 0.7933 - mean_pred: 0.3536
Epoch 705/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3843 - accuracy: 0.7933 - mean_pred: 0.3625
Epoch 706/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3833 - accuracy: 0.7989 - mean_pred: 0.3554
Epoch 707/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3832 - accuracy: 0.7914 - mean_pred: 0.3660
Epoch 708/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3827 - accuracy: 0.7858 - mean_pred: 0.3690
Epoch 709/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3827 - accuracy: 0.7952 - mean_pred: 0.3590
Epoch 710/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3820 - accuracy: 0.7970 - mean_pred: 0.3510
Epoch 711/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3822 - accuracy: 0.7952 - mean_pred: 0.3570
Epoch 712/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3827 - accuracy: 0.7989 - mean_pred: 0.3598
Epoch 713/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3815 - accuracy: 0.7952 - mean_pred: 0.3681
Epoch 714/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3836 - accuracy: 0.7952 - mean_pred: 0.3489
Epoch 715/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3808 - accuracy: 0.7933 - mean_pred: 0.3548
Epoch 716/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3854 - accuracy: 0.7952 - mean_pred: 0.3683
Epoch 717/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3800 - accuracy: 0.7877 - mean_pred: 0.3531
Epoch 718/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3811 - accuracy: 0.7952 - mean_pred: 0.3639
Epoch 719/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3814 - accuracy: 0.7914 - mean_pred: 0.3519
Epoch 720/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3811 - accuracy: 0.7952 - mean_pred: 0.3600
Epoch 721/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3814 - accuracy: 0.7914 - mean_pred: 0.3585
Epoch 722/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3808 - accuracy: 0.7970 - mean_pred: 0.3624
Epoch 723/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3814 - accuracy: 0.7989 - mean_pred: 0.3565
Epoch 724/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3798 - accuracy: 0.7933 - mean_pred: 0.3595
Epoch 725/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3820 - accuracy: 0.8007 - mean_pred: 0.3600
Epoch 726/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3796 - accuracy: 0.7896 - mean_pred: 0.3558
Epoch 727/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3802 - accuracy: 0.7877 - mean_pred: 0.3598
Epoch 728/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3802 - accuracy: 0.7933 - mean_pred: 0.3554
Epoch 729/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3805 - accuracy: 0.7896 - mean_pred: 0.3570
Epoch 730/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3801 - accuracy: 0.7933 - mean_pred: 0.3582
Epoch 731/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3799 - accuracy: 0.7914 - mean_pred: 0.3660
Epoch 732/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3810 - accuracy: 0.8007 - mean_pred: 0.3649
Epoch 733/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3789 - accuracy: 0.7952 - mean_pred: 0.3600
Epoch 734/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3796 - accuracy: 0.7933 - mean_pred: 0.3576
Epoch 735/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3795 - accuracy: 0.7952 - mean_pred: 0.3568
Epoch 736/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3813 - accuracy: 0.7952 - mean_pred: 0.3656
Epoch 737/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3782 - accuracy: 0.7896 - mean_pred: 0.3599
Epoch 738/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3791 - accuracy: 0.7877 - mean_pred: 0.3593
Epoch 739/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3789 - accuracy: 0.7970 - mean_pred: 0.3556
Epoch 740/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3801 - accuracy: 0.7933 - mean_pred: 0.3594
Epoch 741/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3779 - accuracy: 0.7896 - mean_pred: 0.3534
Epoch 742/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3800 - accuracy: 0.7877 - mean_pred: 0.3717
Epoch 743/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3784 - accuracy: 0.7914 - mean_pred: 0.3553
Epoch 744/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3773 - accuracy: 0.7933 - mean_pred: 0.3586
Epoch 745/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3792 - accuracy: 0.7952 - mean_pred: 0.3759
Epoch 746/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3790 - accuracy: 0.7858 - mean_pred: 0.3519
Epoch 747/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3784 - accuracy: 0.7914 - mean_pred: 0.3565
Epoch 748/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3792 - accuracy: 0.7952 - mean_pred: 0.3571
Epoch 749/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3780 - accuracy: 0.7914 - mean_pred: 0.3561
Epoch 750/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3782 - accuracy: 0.7952 - mean_pred: 0.3685
Epoch 751/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3790 - accuracy: 0.7877 - mean_pred: 0.3551
Epoch 752/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3777 - accuracy: 0.7896 - mean_pred: 0.3600
Epoch 753/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3767 - accuracy: 0.7914 - mean_pred: 0.3602
Epoch 754/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3786 - accuracy: 0.7933 - mean_pred: 0.3559
Epoch 755/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3781 - accuracy: 0.7896 - mean_pred: 0.3652
Epoch 756/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3765 - accuracy: 0.8101 - mean_pred: 0.3490
Epoch 757/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3781 - accuracy: 0.7933 - mean_pred: 0.3595
Epoch 758/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3771 - accuracy: 0.7970 - mean_pred: 0.3707
Epoch 759/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3774 - accuracy: 0.7877 - mean_pred: 0.3498
Epoch 760/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3756 - accuracy: 0.7877 - mean_pred: 0.3648
Epoch 761/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3780 - accuracy: 0.7952 - mean_pred: 0.3504
Epoch 762/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3763 - accuracy: 0.7933 - mean_pred: 0.3641
Epoch 763/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3760 - accuracy: 0.7840 - mean_pred: 0.3600
Epoch 764/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3766 - accuracy: 0.7914 - mean_pred: 0.3585
Epoch 765/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3766 - accuracy: 0.7952 - mean_pred: 0.3450
Epoch 766/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3768 - accuracy: 0.7970 - mean_pred: 0.3698
Epoch 767/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3760 - accuracy: 0.7877 - mean_pred: 0.3569
Epoch 768/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3753 - accuracy: 0.7952 - mean_pred: 0.3573
Epoch 769/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3749 - accuracy: 0.7989 - mean_pred: 0.3569
Epoch 770/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3757 - accuracy: 0.8007 - mean_pred: 0.3648
Epoch 771/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3750 - accuracy: 0.7896 - mean_pred: 0.3587
Epoch 772/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3748 - accuracy: 0.7933 - mean_pred: 0.3604
Epoch 773/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3752 - accuracy: 0.7858 - mean_pred: 0.3604
Epoch 774/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3760 - accuracy: 0.7933 - mean_pred: 0.3645
Epoch 775/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3735 - accuracy: 0.7896 - mean_pred: 0.3588
Epoch 776/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3749 - accuracy: 0.7933 - mean_pred: 0.3651
Epoch 777/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3756 - accuracy: 0.7933 - mean_pred: 0.3573
Epoch 778/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3742 - accuracy: 0.8007 - mean_pred: 0.3555
Epoch 779/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3736 - accuracy: 0.7933 - mean_pred: 0.3617
Epoch 780/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3743 - accuracy: 0.7896 - mean_pred: 0.3563
Epoch 781/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3734 - accuracy: 0.7914 - mean_pred: 0.3574
Epoch 782/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3731 - accuracy: 0.8007 - mean_pred: 0.3604
Epoch 783/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3753 - accuracy: 0.7914 - mean_pred: 0.3589
Epoch 784/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3739 - accuracy: 0.7933 - mean_pred: 0.3555
Epoch 785/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3732 - accuracy: 0.7970 - mean_pred: 0.3620
Epoch 786/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3729 - accuracy: 0.7970 - mean_pred: 0.3579
Epoch 787/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3733 - accuracy: 0.8007 - mean_pred: 0.3644
Epoch 788/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3714 - accuracy: 0.8007 - mean_pred: 0.3537
Epoch 789/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3718 - accuracy: 0.8007 - mean_pred: 0.3605
Epoch 790/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3727 - accuracy: 0.7933 - mean_pred: 0.3623
Epoch 791/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3718 - accuracy: 0.7970 - mean_pred: 0.3600
Epoch 792/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3729 - accuracy: 0.7970 - mean_pred: 0.3562
Epoch 793/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3712 - accuracy: 0.8007 - mean_pred: 0.3527
Epoch 794/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3732 - accuracy: 0.7914 - mean_pred: 0.3645
Epoch 795/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3718 - accuracy: 0.7989 - mean_pred: 0.3636
Epoch 796/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3695 - accuracy: 0.8045 - mean_pred: 0.3529
Epoch 797/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3714 - accuracy: 0.7952 - mean_pred: 0.3705
Epoch 798/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3716 - accuracy: 0.7970 - mean_pred: 0.3602
Epoch 799/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3714 - accuracy: 0.7952 - mean_pred: 0.3621
Epoch 800/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3705 - accuracy: 0.8101 - mean_pred: 0.3523
Epoch 801/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3698 - accuracy: 0.7952 - mean_pred: 0.3647
Epoch 802/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3699 - accuracy: 0.7952 - mean_pred: 0.3491
Epoch 803/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3717 - accuracy: 0.7970 - mean_pred: 0.3635
Epoch 804/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3692 - accuracy: 0.8026 - mean_pred: 0.3705
Epoch 805/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3704 - accuracy: 0.8026 - mean_pred: 0.3506
Epoch 806/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3701 - accuracy: 0.7914 - mean_pred: 0.3577
Epoch 807/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3698 - accuracy: 0.7970 - mean_pred: 0.3583
Epoch 808/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3701 - accuracy: 0.7896 - mean_pred: 0.3582
Epoch 809/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3713 - accuracy: 0.7952 - mean_pred: 0.3565
Epoch 810/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3692 - accuracy: 0.7933 - mean_pred: 0.3638
Epoch 811/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3708 - accuracy: 0.7989 - mean_pred: 0.3562
Epoch 812/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3706 - accuracy: 0.7933 - mean_pred: 0.3584
Epoch 813/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3688 - accuracy: 0.8026 - mean_pred: 0.3635
Epoch 814/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3691 - accuracy: 0.7989 - mean_pred: 0.3611
Epoch 815/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3709 - accuracy: 0.7896 - mean_pred: 0.3575
Epoch 816/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3707 - accuracy: 0.8007 - mean_pred: 0.3559
Epoch 817/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3696 - accuracy: 0.7989 - mean_pred: 0.3462
Epoch 818/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3712 - accuracy: 0.7933 - mean_pred: 0.3631
Epoch 819/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3696 - accuracy: 0.7989 - mean_pred: 0.3610
Epoch 820/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3681 - accuracy: 0.7989 - mean_pred: 0.3572
Epoch 821/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3686 - accuracy: 0.8082 - mean_pred: 0.3644
Epoch 822/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3673 - accuracy: 0.7989 - mean_pred: 0.3556
Epoch 823/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3682 - accuracy: 0.7952 - mean_pred: 0.3573
Epoch 824/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3698 - accuracy: 0.7989 - mean_pred: 0.3679
Epoch 825/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3683 - accuracy: 0.7933 - mean_pred: 0.3583
Epoch 826/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3665 - accuracy: 0.7952 - mean_pred: 0.3572
Epoch 827/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3679 - accuracy: 0.7952 - mean_pred: 0.3787
Epoch 828/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3672 - accuracy: 0.8007 - mean_pred: 0.3495
Epoch 829/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3673 - accuracy: 0.7970 - mean_pred: 0.3674
Epoch 830/1000
537/537 [==============================] - 0s 13us/step - loss: 0.3689 - accuracy: 0.8045 - mean_pred: 0.3454
Epoch 831/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3670 - accuracy: 0.7970 - mean_pred: 0.3660
Epoch 832/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3681 - accuracy: 0.8082 - mean_pred: 0.3576
Epoch 833/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3671 - accuracy: 0.7933 - mean_pred: 0.3584
Epoch 834/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3671 - accuracy: 0.8101 - mean_pred: 0.3629
Epoch 835/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3694 - accuracy: 0.7970 - mean_pred: 0.3575
Epoch 836/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3660 - accuracy: 0.8007 - mean_pred: 0.3512
Epoch 837/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3662 - accuracy: 0.8026 - mean_pred: 0.3573
Epoch 838/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3672 - accuracy: 0.7989 - mean_pred: 0.3533
Epoch 839/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3661 - accuracy: 0.7989 - mean_pred: 0.3624
Epoch 840/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3674 - accuracy: 0.7896 - mean_pred: 0.3586
Epoch 841/1000
537/537 [==============================] - 0s 13us/step - loss: 0.3661 - accuracy: 0.7989 - mean_pred: 0.3584
Epoch 842/1000
537/537 [==============================] - 0s 13us/step - loss: 0.3672 - accuracy: 0.7970 - mean_pred: 0.3629
Epoch 843/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3653 - accuracy: 0.8026 - mean_pred: 0.3609
Epoch 844/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3651 - accuracy: 0.8045 - mean_pred: 0.3670
Epoch 845/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3662 - accuracy: 0.8045 - mean_pred: 0.3538
Epoch 846/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3661 - accuracy: 0.8063 - mean_pred: 0.3614
Epoch 847/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3652 - accuracy: 0.8026 - mean_pred: 0.3555
Epoch 848/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3660 - accuracy: 0.7989 - mean_pred: 0.3590
Epoch 849/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3660 - accuracy: 0.8026 - mean_pred: 0.3582
Epoch 850/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3648 - accuracy: 0.8063 - mean_pred: 0.3557
Epoch 851/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3647 - accuracy: 0.8026 - mean_pred: 0.3640
Epoch 852/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3649 - accuracy: 0.8138 - mean_pred: 0.3596
Epoch 853/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3658 - accuracy: 0.7970 - mean_pred: 0.3499
Epoch 854/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3652 - accuracy: 0.8026 - mean_pred: 0.3581
Epoch 855/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3638 - accuracy: 0.7989 - mean_pred: 0.3588
Epoch 856/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3640 - accuracy: 0.7970 - mean_pred: 0.3549
Epoch 857/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3653 - accuracy: 0.7989 - mean_pred: 0.3689
Epoch 858/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3663 - accuracy: 0.8026 - mean_pred: 0.3628
Epoch 859/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3638 - accuracy: 0.8026 - mean_pred: 0.3562
Epoch 860/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3633 - accuracy: 0.8007 - mean_pred: 0.3576
Epoch 861/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3628 - accuracy: 0.8045 - mean_pred: 0.3594
Epoch 862/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3654 - accuracy: 0.7989 - mean_pred: 0.3503
Epoch 863/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3653 - accuracy: 0.7989 - mean_pred: 0.3567
Epoch 864/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3632 - accuracy: 0.7970 - mean_pred: 0.3663
Epoch 865/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3644 - accuracy: 0.7952 - mean_pred: 0.3587
Epoch 866/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3622 - accuracy: 0.8138 - mean_pred: 0.3459
Epoch 867/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3635 - accuracy: 0.7970 - mean_pred: 0.3594
Epoch 868/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3640 - accuracy: 0.7989 - mean_pred: 0.3618
Epoch 869/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3615 - accuracy: 0.8026 - mean_pred: 0.3546
Epoch 870/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3639 - accuracy: 0.7933 - mean_pred: 0.3599
Epoch 871/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3626 - accuracy: 0.7933 - mean_pred: 0.3610
Epoch 872/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3629 - accuracy: 0.8063 - mean_pred: 0.3649
Epoch 873/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3622 - accuracy: 0.8063 - mean_pred: 0.3523
Epoch 874/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3643 - accuracy: 0.7952 - mean_pred: 0.3658
Epoch 875/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3614 - accuracy: 0.7952 - mean_pred: 0.3546
Epoch 876/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3631 - accuracy: 0.7952 - mean_pred: 0.3647
Epoch 877/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3619 - accuracy: 0.8026 - mean_pred: 0.3575
Epoch 878/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3622 - accuracy: 0.8045 - mean_pred: 0.3607
Epoch 879/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3639 - accuracy: 0.8063 - mean_pred: 0.3590
Epoch 880/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3617 - accuracy: 0.7989 - mean_pred: 0.3549
Epoch 881/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3615 - accuracy: 0.8026 - mean_pred: 0.3591
Epoch 882/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3610 - accuracy: 0.7989 - mean_pred: 0.3603
Epoch 883/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3626 - accuracy: 0.8045 - mean_pred: 0.3549
Epoch 884/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3609 - accuracy: 0.8007 - mean_pred: 0.3553
Epoch 885/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3599 - accuracy: 0.8007 - mean_pred: 0.3698
Epoch 886/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3606 - accuracy: 0.8026 - mean_pred: 0.3572
Epoch 887/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3613 - accuracy: 0.7989 - mean_pred: 0.3474
Epoch 888/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3602 - accuracy: 0.8026 - mean_pred: 0.3639
Epoch 889/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3603 - accuracy: 0.8082 - mean_pred: 0.3552
Epoch 890/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3620 - accuracy: 0.8045 - mean_pred: 0.3595
Epoch 891/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3613 - accuracy: 0.8007 - mean_pred: 0.3521
Epoch 892/1000
537/537 [==============================] - 0s 13us/step - loss: 0.3599 - accuracy: 0.8045 - mean_pred: 0.3639
Epoch 893/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3590 - accuracy: 0.8101 - mean_pred: 0.3547
Epoch 894/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3612 - accuracy: 0.7989 - mean_pred: 0.3627
Epoch 895/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3605 - accuracy: 0.8007 - mean_pred: 0.3574
Epoch 896/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3605 - accuracy: 0.8045 - mean_pred: 0.3555
Epoch 897/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3615 - accuracy: 0.8007 - mean_pred: 0.3634
Epoch 898/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3586 - accuracy: 0.8082 - mean_pred: 0.3481
Epoch 899/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3612 - accuracy: 0.7970 - mean_pred: 0.3687
Epoch 900/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3584 - accuracy: 0.7989 - mean_pred: 0.3462
Epoch 901/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3572 - accuracy: 0.7933 - mean_pred: 0.3628
Epoch 902/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3597 - accuracy: 0.8045 - mean_pred: 0.3611
Epoch 903/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3592 - accuracy: 0.7952 - mean_pred: 0.3628
Epoch 904/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3566 - accuracy: 0.8007 - mean_pred: 0.3526
Epoch 905/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3611 - accuracy: 0.8045 - mean_pred: 0.3615
Epoch 906/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3602 - accuracy: 0.7989 - mean_pred: 0.3562
Epoch 907/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3595 - accuracy: 0.8045 - mean_pred: 0.3641
Epoch 908/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3574 - accuracy: 0.8045 - mean_pred: 0.3497
Epoch 909/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3588 - accuracy: 0.8007 - mean_pred: 0.3648
Epoch 910/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3572 - accuracy: 0.8007 - mean_pred: 0.3527
Epoch 911/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3580 - accuracy: 0.7952 - mean_pred: 0.3591
Epoch 912/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3597 - accuracy: 0.7970 - mean_pred: 0.3608
Epoch 913/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3565 - accuracy: 0.8063 - mean_pred: 0.3517
Epoch 914/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3599 - accuracy: 0.7989 - mean_pred: 0.3631
Epoch 915/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3575 - accuracy: 0.8045 - mean_pred: 0.3625
Epoch 916/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3580 - accuracy: 0.7970 - mean_pred: 0.3616
Epoch 917/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3559 - accuracy: 0.8063 - mean_pred: 0.3660
Epoch 918/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3587 - accuracy: 0.8007 - mean_pred: 0.3541
Epoch 919/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3561 - accuracy: 0.8026 - mean_pred: 0.3575
Epoch 920/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3569 - accuracy: 0.8063 - mean_pred: 0.3535
Epoch 921/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3590 - accuracy: 0.8082 - mean_pred: 0.3681
Epoch 922/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3543 - accuracy: 0.8026 - mean_pred: 0.3548
Epoch 923/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3568 - accuracy: 0.8007 - mean_pred: 0.3549
Epoch 924/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3573 - accuracy: 0.7970 - mean_pred: 0.3657
Epoch 925/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3552 - accuracy: 0.8063 - mean_pred: 0.3617
Epoch 926/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3578 - accuracy: 0.7970 - mean_pred: 0.3548
Epoch 927/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3544 - accuracy: 0.8063 - mean_pred: 0.3585
Epoch 928/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3564 - accuracy: 0.8007 - mean_pred: 0.3557
Epoch 929/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3557 - accuracy: 0.7952 - mean_pred: 0.3629
Epoch 930/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3554 - accuracy: 0.7970 - mean_pred: 0.3527
Epoch 931/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3560 - accuracy: 0.8026 - mean_pred: 0.3548
Epoch 932/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3546 - accuracy: 0.8026 - mean_pred: 0.3632
Epoch 933/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3573 - accuracy: 0.7970 - mean_pred: 0.3595
Epoch 934/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3553 - accuracy: 0.8082 - mean_pred: 0.3593
Epoch 935/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3556 - accuracy: 0.7989 - mean_pred: 0.3607
Epoch 936/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3536 - accuracy: 0.8082 - mean_pred: 0.3576
Epoch 937/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3549 - accuracy: 0.8026 - mean_pred: 0.3711
Epoch 938/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3548 - accuracy: 0.8045 - mean_pred: 0.3535
Epoch 939/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3541 - accuracy: 0.8063 - mean_pred: 0.3594
Epoch 940/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3526 - accuracy: 0.8026 - mean_pred: 0.3645
Epoch 941/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3543 - accuracy: 0.8026 - mean_pred: 0.3513
Epoch 942/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3552 - accuracy: 0.8082 - mean_pred: 0.3643
Epoch 943/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3522 - accuracy: 0.8063 - mean_pred: 0.3555
Epoch 944/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3526 - accuracy: 0.8138 - mean_pred: 0.3551
Epoch 945/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3529 - accuracy: 0.8082 - mean_pred: 0.3699
Epoch 946/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3549 - accuracy: 0.8138 - mean_pred: 0.3558
Epoch 947/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3530 - accuracy: 0.7970 - mean_pred: 0.3609
Epoch 948/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3540 - accuracy: 0.7970 - mean_pred: 0.3628
Epoch 949/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3536 - accuracy: 0.8082 - mean_pred: 0.3527
Epoch 950/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3544 - accuracy: 0.8026 - mean_pred: 0.3605
Epoch 951/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3529 - accuracy: 0.8119 - mean_pred: 0.3618
Epoch 952/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3521 - accuracy: 0.8063 - mean_pred: 0.3659
Epoch 953/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3543 - accuracy: 0.8082 - mean_pred: 0.3510
Epoch 954/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3535 - accuracy: 0.8063 - mean_pred: 0.3574
Epoch 955/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3516 - accuracy: 0.8063 - mean_pred: 0.3637
Epoch 956/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3514 - accuracy: 0.8119 - mean_pred: 0.3653
Epoch 957/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3528 - accuracy: 0.8045 - mean_pred: 0.3552
Epoch 958/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3530 - accuracy: 0.8026 - mean_pred: 0.3650
Epoch 959/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3519 - accuracy: 0.8119 - mean_pred: 0.3594
Epoch 960/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3513 - accuracy: 0.8082 - mean_pred: 0.3561
Epoch 961/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3506 - accuracy: 0.8045 - mean_pred: 0.3593
Epoch 962/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3522 - accuracy: 0.8082 - mean_pred: 0.3779
Epoch 963/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3505 - accuracy: 0.8156 - mean_pred: 0.3525
Epoch 964/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3517 - accuracy: 0.8082 - mean_pred: 0.3671
Epoch 965/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3512 - accuracy: 0.8119 - mean_pred: 0.3589
Epoch 966/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3512 - accuracy: 0.8063 - mean_pred: 0.3658
Epoch 967/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3511 - accuracy: 0.8101 - mean_pred: 0.3576
Epoch 968/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3509 - accuracy: 0.8101 - mean_pred: 0.3617
Epoch 969/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3504 - accuracy: 0.8026 - mean_pred: 0.3718
Epoch 970/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3524 - accuracy: 0.8026 - mean_pred: 0.3596
Epoch 971/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3505 - accuracy: 0.8101 - mean_pred: 0.3615
Epoch 972/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3503 - accuracy: 0.8101 - mean_pred: 0.3659
Epoch 973/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3505 - accuracy: 0.8138 - mean_pred: 0.3479
Epoch 974/1000
537/537 [==============================] - 0s 19us/step - loss: 0.3517 - accuracy: 0.8045 - mean_pred: 0.3609
Epoch 975/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3500 - accuracy: 0.8007 - mean_pred: 0.3578
Epoch 976/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3498 - accuracy: 0.8063 - mean_pred: 0.3626
Epoch 977/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3483 - accuracy: 0.8101 - mean_pred: 0.3678
Epoch 978/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3513 - accuracy: 0.8138 - mean_pred: 0.3520
Epoch 979/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3523 - accuracy: 0.8082 - mean_pred: 0.3631
Epoch 980/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3475 - accuracy: 0.8082 - mean_pred: 0.3621
Epoch 981/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3501 - accuracy: 0.8063 - mean_pred: 0.3621
Epoch 982/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3493 - accuracy: 0.8101 - mean_pred: 0.3598
Epoch 983/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3509 - accuracy: 0.8082 - mean_pred: 0.3759
Epoch 984/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3482 - accuracy: 0.8082 - mean_pred: 0.3519
Epoch 985/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3501 - accuracy: 0.8026 - mean_pred: 0.3657
Epoch 986/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3470 - accuracy: 0.8156 - mean_pred: 0.3608
Epoch 987/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3517 - accuracy: 0.8045 - mean_pred: 0.3647
Epoch 988/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3495 - accuracy: 0.8082 - mean_pred: 0.3540
Epoch 989/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3481 - accuracy: 0.8082 - mean_pred: 0.3690
Epoch 990/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3486 - accuracy: 0.8119 - mean_pred: 0.3503
Epoch 991/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3492 - accuracy: 0.8138 - mean_pred: 0.3665
Epoch 992/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3484 - accuracy: 0.8045 - mean_pred: 0.3651
Epoch 993/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3494 - accuracy: 0.8026 - mean_pred: 0.3626
Epoch 994/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3483 - accuracy: 0.8101 - mean_pred: 0.3631
Epoch 995/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3472 - accuracy: 0.8175 - mean_pred: 0.3658
Epoch 996/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3473 - accuracy: 0.8082 - mean_pred: 0.3585
Epoch 997/1000
537/537 [==============================] - 0s 15us/step - loss: 0.3492 - accuracy: 0.8156 - mean_pred: 0.3621
Epoch 998/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3460 - accuracy: 0.8119 - mean_pred: 0.3769
Epoch 999/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3494 - accuracy: 0.8101 - mean_pred: 0.3550
Epoch 1000/1000
537/537 [==============================] - 0s 17us/step - loss: 0.3458 - accuracy: 0.8119 - mean_pred: 0.3574
231/231 [==============================] - 0s 82us/step
In [13]:
model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 12)                108       
_________________________________________________________________
dense_2 (Dense)              (None, 10)                130       
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 11        
=================================================================
Total params: 249
Trainable params: 249
Non-trainable params: 0
_________________________________________________________________

Accuracy

In [14]:
score = pd.DataFrame(score, index = model.metrics_names).T
history = pd.DataFrame(history.history)
display(score.style.hide_index())
loss accuracy mean_pred
1.284564 0.735931 0.430036
In [15]:
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
_ = ax.plot(history['accuracy'], 'navy', label='Accuracy', linewidth=2)
_ = ax.plot(history['loss'], 'red', label='Loss', linewidth=2)
_ = ax.set_yscale('log')
_ = ax.set_xlim(left = 0, right = N)
_ = ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize = 14)
_ = ax.set_xlabel('Steps', fontsize = 14)

As expected, the accuracy and loss improve as step number increases.

A Graph of the Model

In [16]:
plot_model(model, show_shapes=True, show_layer_names=True, expand_nested = True)
Out[16]: